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MCAUPF (Maximum Correntropy Adaptive Unscented Particle Filter) based target tracking method

A technology of particle filter and target tracking, which is applied in radio wave reflection/re-radiation, radio wave measurement system, instrument, etc., can solve problems such as large amount of calculation and difficulty in realization

Inactive Publication Date: 2018-12-11
HARBIN ENG UNIV
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Problems solved by technology

[0004] In addition, when using traditional PF for calculation, the amount of calculation is very large, so it is no longer suitable for occasions with high real-time requirements
For this reason, a PF algorithm based on Kullback–Leibler distance (KLD) sampling is proposed, but this method assumes that all particles come from the real posterior density function, which is difficult to implement in practical applications

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  • MCAUPF (Maximum Correntropy Adaptive Unscented Particle Filter) based target tracking method
  • MCAUPF (Maximum Correntropy Adaptive Unscented Particle Filter) based target tracking method
  • MCAUPF (Maximum Correntropy Adaptive Unscented Particle Filter) based target tracking method

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[0053] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0054] A kind of target tracking method based on MCAUPF of the present invention, flow chart such as figure 1 shown, including the following steps:

[0055] Step 1: Establish the state equation and observation equation describing the target tracking system as follows:

[0056]

[0057] Among them, k-1 means the k-1th moment, k means the kth moment, x k is the state vector of the n-dimensional tracking target parameters at the kth moment, y k is the measurement vector of the m-dimensional tracking target parameters at the k+1th moment, f( ) and h( ) are known nonlinear functions, w k-1 is the n-dimensional system noise at the k-1th moment, v k is the m-dimensional measurement noise at the kth moment, assuming that the system noise obeys the Gaussian distribution w k-1 ~N(0,Q k-1 ), the measurement noise contains outliers and o...

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Abstract

The invention provides an MCAUPF based target tracking method. An MCAUPF is used to complete state estimation in the target tracking process. In the target tracking process, target tracking equation and measurement equations are reconstructed into a nonlinear recursion model, in the framework of unscented particle filtering, a significance probability density function needed by filtering particlesgenerated by maximum correntropy unscented Kalman filtering is used, a Kullback-Leibler distance resampling method is used to resample the generated particles, the state of the tracking target is estimated according to a UPF algorithm flow, and the target is tracked in real time. The MCAUPF method is applied to target tracking in which outliers occur in noise measurement, the precision is highercompared with that of present PF, improved PF and robust filtering, and the computing complexity is lower than that of a present improved particle filter algorithm.

Description

technical field [0001] The invention relates to a target tracking method based on the maximum cross-correlation entropy self-adaptive unscented particle filter, which is suitable for nonlinear systems with heavy tail measurement noise, and belongs to the technical field of nonlinear robust filtering and target tracking. Background technique [0002] When using particle filter (Particle Filter, PF) to track the target, due to the maneuvering of the target, abnormal mutation points of measurement data, sensor failure, measurement loss and intentional interference, etc., the measurement noise will appear outliers, which makes The radar's tracking accuracy of the target is seriously affected. To solve this problem, it is proposed to use Huber Unscented Particle Filter (HRUPF) to deal with it, but because the influence function in HRUPF will not fall back after the influence parameter γ exceeds 1.345, the estimation performance will decrease. In response to this problem, people ...

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Application Information

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IPC IPC(8): G01S13/72
CPCG01S13/72
Inventor 张勇刚范颖王国庆汪晓雨李宁
Owner HARBIN ENG UNIV
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